AI 中文总结
研究5G NR信道估计问题，提出CHEA方法，通过多分辨率窗口设计降低计算成本，单个模型支持不同PRB分配，在PUSCH上实现低MSE且操作比现有注意力估计器低，提升了信道估计性能。
AI 中文摘要
基于注意力的神经估计器能实现强大的信道估计精度，但时频资源网格上全局注意力的计算成本随子载波数量呈二次增长，且这些估计器通常与单一资源分配相关。本文提出用于5G新无线电（5G NR）多用户多输入多输出（MU-MIMO）的低复杂度信道估计注意力（CHEA）。CHEA用多分辨率窗口设计取代全局注意力，包括高分辨率编码器保留局部导频细节、低分辨率编码器捕获更宽频域上下文、局部交叉注意力解码器将粗略上下文传回高分辨率导频令牌，每个物理资源块（PRB）上采样模块在整个时隙重建信道。由于注意力操作限于固定大小窗口且按PRB重建，CHEA成本随子载波数量线性增长，单个训练模型支持不同PRB分配无需重新训练。在符合标准的物理上行链路共享信道（PUSCH）上，CHEA在传统和现有神经估计器中实现最低均方误差（MSE），操作比现有基于注意力的估计器低2.8倍至22.0倍。
英文摘要
Attention-based neural estimators achieve strong channel-estimation accuracy, but the computational cost of global attention over the time-frequency resource grid grows quadratically with the number of subcarriers, and these estimators are typically tied to a single resource allocation. This paper proposes Channel Estimation Attention (CHEA), a low-complexity channel estimator for 5G New Radio (5G NR) multi-user multiple-input multiple-output (MU-MIMO). CHEA replaces global attention with a multi-resolution windowed design: a high-resolution encoder preserves local pilot detail, a low-resolution encoder captures wider frequency-domain context, and a local cross-attention decoder transfers this coarse context back to the high-resolution pilot tokens. A per-Physical Resource Block (PRB) upsampling module then reconstructs the channel over the full slot. Because every attention operation is confined to a fixed-size window and reconstruction is performed per PRB, the cost of CHEA scales linearly with the number of subcarriers, and a single trained model supports different PRB allocations without retraining. On a standard-compliant Physical Uplink Shared Channel (PUSCH), CHEA achieves the lowest Mean Squared Error (MSE) among conventional and state-of-the-art neural estimators, while requiring 2.8\(\times\) to 22.0\(\times\) lower operations than existing attention-based estimators.
Comments2026 IEEE PIMRC Workshops
